1. Field of the Invention
The technology described herein relates to processing information from a database, and more particularly relates to using such information to perform modeling.
2. Background Art
It is a common need among analysts, whether financial or technological, to create models based on data that may reside in a database. A financial analyst, for example, may rely on a database that contains information corresponding to one or more portfolios, each of which has some number of holdings. Each holding in turn may have some name, a related price per share, and an indication of the number of shares held. The analyst may then desire to take this information, and create one or more hypothetical portfolios. The analyst can then analyze the information, and manipulate it to vary holdings, amounts held, or prices. The analyst may then be able to project future performance, for example, or otherwise track changes to the model, based on particular performance parameters.
It may be undesirable, however, to make reflect such changes in the underlying database. The data therein may be needed by other analysts, or may otherwise be needed for future reference. Moreover, the information in the database may represent historical data that needs to be preserved. In addition, it would be undesirable to allow the manipulations performed on a given model to impact other models that may have been created by other analysts using the same database. Yet, it may also be desirable to share a given model with other analysts.
There are no available solutions that can deal with these problems, in a practical way. A simple spreadsheet may be used, for example. But if the amount of data is significant, scalability issues arise in light of the amount of memory and processing capability that would be needed. A given index fund, for example, may have thousands of records. This is compounded by the fact that there may be a need to model tens of thousands of portfolios.
While such a solution could be implemented using memory media, such as random access memory, it should also be kept in mind that most of the values that would be imported from a database will likely remain unchanged throughout the modeling process. This would represent a significant waste of memory for information that is never manipulated. Given the large amount of memory that may be required, and the possible need for multiple computing platforms on which to perform the modeling, such a solution is not realistic.
What is needed, therefore, is a solution that is scalable, not memory intensive, and does not impact the underlying database or modeling performed by other analysts.
Further embodiments, features, and advantages of the present invention, as well as the operation of the various embodiments of the present invention, are described below with reference to the accompanying drawings.